Hyperbolic Decline Parameters During and After Linear Flow: Field Example from the Barnett Shale Using Public Data
Bibliographic record
Abstract
Abstract The Barnett Shale is one of the first unconventional shale plays developed with multistaged, fracture-stimulated horizontal wells in the world. It is located in North Central Texas near Fort Worth. At the end of 2013, the Barnett Shale had over 14,000 multistaged hydraulically fractured horizontal wells (MFHW) with approximately 7,600 of these wells with over five years of production history. In addition to these MFHW, there are approximately 4,000 vertical wells. Production forecasting for unconventional reservoirs with MFHW is a topic with a great amount of interest. The question is what are the appropriate decline parameters to be used in the forecast? Are multisegment forecasts with their own decline parameters necessary? Currently, production forecasting using a modified hyperbolic Arps equation is still widely accepted. This work provides analysis in characterizing decline parameters during and after linear flow for horizontal wells in the Barnett Shale using public data. There will be examples of MFHWs from the Barnett where the hyperbolic b-exponent will be calculated for each month of production and shown to vary with time as flow regimes change. Single well simulation will be used to characterize the different flow regimes and their effect on decline parameters. Simulation of wells with and without volume outside of fracture tips and their effect on decline parameters will be shown. The decline parameters were in an Arps forecast to match our single well simulation forecast. Uncertainty analysis of production forecast using simulation models is also presented in this work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".